Intellectual Property India Publishes Patent Application for Ai-Enabled Social Influence And Consumer Purchase Behavior Prediction And Adaptive Recommendation System

MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641105410 A) filed by Sr University on September 01, 2026, for Ai-Enabled Social Influence And Consumer Purchase Behavior Prediction And Adaptive Recommendation System.

Inventors include Padmini Nawandar; and Dr. Karra.

The application for the patent was published on September 11, 2026, under issue no. 37/2026.

Abstract: The present invention relates to an AI-enabled system and method for predicting consumer purchase behavior and generating adaptive product recommendations using social media data. The system analyzes multimodal social media information including text, images, videos, comments, reactions, shares, influencer interactions, social relationships, and temporal engagement patterns. A multimodal processing mechanism extracts product information, consumer opinions, sentiment, and purchase- related signals from heterogeneous social media content. A social influence analysis mechanism determines the credibility and category-specific influence of social participants by evaluating interaction quality, relationship strength, engagement behavior, and historical recommendation performance rather than relying solely on follower counts or popularity. A consumer purchase-intent engine continuously evaluates consumer interactions and generates a dynamic purchase-intent state. A temporal analysis mechanism assigns varying significance to social signals according to their recency, engagement velocity, and persistence. The resulting social influence, purchase intent, sentiment, product relevance, and engagement characteristics are integrated into a dynamic consumer decision state, which is used by an adaptive recommendation engine to provide context-specific product information and recommendations. A feedback-learning mechanism further updates the prediction and recommendation models based on subsequent consumer responses, thereby enabling continuous adaptation and improved prediction of consumer purchase behavior.

Disclaimer: Curated by HT Syndication.